Neuro-dynamic programming for fractionated radiotherapy planning

We investigate an on-line planning strategy for the fractionated radiotherapy planning problem, which incorporates the effects of day-to-day patient motion. On-line planning demonstrates significant improvement over off-line strategies in terms of reducing registration error, but it requires extra work in the replanning procedures, such as in the CT scans and the re-computation of a deliverable dose profile. We formulate the problem in a dynamic programming framework and solve it based on the approximate policy iteration techniques of neuro-dynamic programming. In initial limited testing, the solutions we obtain outperform existing solutions and offer an improved dose profile for each fraction of the treatment.

[1]  John M. Wilson,et al.  Introduction to Stochastic Programming , 1998, J. Oper. Res. Soc..

[2]  S. Ioffe,et al.  Temporal Differences-Based Policy Iteration and Applications in Neuro-Dynamic Programming , 1996 .

[3]  John N. Tsitsiklis,et al.  Neuro-Dynamic Programming , 1996, Encyclopedia of Machine Learning.

[4]  S. Webb.  The Physics of Conformal Radiotherapy , 1997 .

[5]  Abhijit Gosavi,et al.  Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning , 2003 .

[6]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[7]  L. Verhey,et al.  Immobilizing and Positioning Patients for Radiotherapy. , 1995, Seminars in radiation oncology.

[8]  A. Niemierko,et al.  Optimization of 3D radiation therapy with both physical and biological end points and constraints. , 1992, International journal of radiation oncology, biology, physics.

[9]  Meta M. Voelker.  OPTIMIZATION OF SLICE MODELS , 2002 .

[10]  Dimitri P. Bertsekas,et al.  Temporal Dierences-Based Policy Iteration and Applications in Neuro-Dynamic Programming 1 , 1997 .

[11]  Jonathan G. Li,et al.  Inverse planning incorporating organ motion. , 2000 .

[12]  I A D Bruinvis.  3D Conformal Radiation Therapy: Multimedia Introduction to Methods and Techniques , 2003 .

[13]  A G Visser,et al.  A quality control study of the accuracy of patient positioning in irradiation of pelvic fields. , 1996, International Journal of Radiation Oncology, Biology, Physics.

[14]  Michael C. Ferris,et al.  Radiosurgery Treatment Planning via Nonlinear Programming , 2003, Ann. Oper. Res..

[15]  K. Langen,et al.  Organ motion and its management. , 2001, International journal of radiation oncology, biology, physics.

[16]  J. Unkelbach,et al.  Inclusion of organ movements in IMRT treatment planning via inverse planning based on probability distributions. , 2004, Physics in medicine and biology.

[17]  U Oelfke,et al.  Incorporating organ movements in inverse planning: assessing dose uncertainties by Bayesian inference , 2005, Physics in medicine and biology.

[18]  S. Webb.  The Physics of Conformal Radiotherapy: Advances in Technology , 1997 .

[19]  T E Schultheiss,et al.  An evaluation of setup uncertainties for patients treated to pelvic sites. , 1995, International journal of radiation oncology, biology, physics.

[20]  F Nüsslin,et al.  Adapting inverse planning to patient and organ geometrical variation: algorithm and implementation. , 2003, Medical physics.

[21]  Michael C. Ferris,et al.  Digital Object Identifier (DOI) 10.1007/s10107-004-0530-y , 2004 .

[22]  Andreas Mahr,et al.  3D Conformal Radiation Therapy: Multimedia Introduction to Methods and Techniques , 2007 .

[23]  R. Wets,et al.  Stochastic programming , 1989 .